• Journal of Convergence for Information Technology
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• v.9 no.3
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• pp.22-29
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• 2019

Due to the development of ICT technology, convergence reality contents are utilized as technology for providing services in various industrial fields by visualizing various information such as sensor information and shared information in a service platform showing only simple three-dimensional contents. Research is underway to reduce the weight of applications by transmitting the resources of the object to be enhanced to the network as the information and the contents to be provided increase. In order to provide resources through the network, servers for processing various information such as pattern information, content information, and sensor information must be constructed in a cloud environment. However, in order to authenticate data transmitted and received in real-time in a cloud environment, there is a problem in that the processing is delayed and a delay phenomenon occurs in the rendering process and QoS is lowered. In this paper, we propose a system to distribute cloud server which provides augmented contents of convergent reality service that provides various contents such as sensor information and three - dimensional model, and shorten the processing time of reliable data through distributed relay between servers Respectively.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.324-325
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• 2016

The sensor network-based service collects data by using the sensor, the data is aware of the situation via the analysis, and the service provider provides a service suitable for the user via the context-awareness. However, this data is generated, it is difficult to match the metadata and standard units. The data integration is required to use the data generated by the different specifications of the sensor efficiently. Accordingly, in this paper we propose a method using an ontology as a method to integrate the data generated by the existing sensors and the new sensor. The ontology is mapping to the standard item and sensors, also include a type and structural difference. The mapping is comprised of two:data mapping, and metadata mapping. There are standard items that are created in this way, type of data exchange between services. This can solve the heterogeneous problem generated by sensors.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.191-199
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• 2023

In this paper, we propose a method to extract the features of five sensor-only facilities built as infrastructure for autonomous cooperative driving, which are from point cloud data acquired by LiDAR. In the case of image acquisition sensors installed in autonomous vehicles, the acquisition data is inconsistent due to the climatic environment and camera characteristics, so LiDAR sensor was applied to replace them. In addition, high-intensity reflectors were designed and attached to each facility to make it easier to distinguish it from other existing facilities with LiDAR. From the five sensor-only facilities developed and the point cloud data acquired by the data acquisition system, feature points were extracted based on the average reflective intensity of the high-intensity reflective paper attached to the facility, clustered by the DBSCAN method, and changed to two-dimensional coordinates by a projection method. The features of the facility at each distance consist of three-dimensional point coordinates, two-dimensional projected coordinates, and reflection intensity, and will be used as training data for a model for facility recognition to be developed in the future.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06b
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• pp.17-20
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• 2011

This paper presents an approach for integrating u-Life care applications into the Cloud computing based on virtual resources to support real-time services and to improve quality of service (QoS) requirement. We propose an architecture for virtualization resources scheduling. The proposed is based on the concepts of Cloud computing and Wireless sensor networks. In this paper, we focus on the scheduling u-Life care applications run on the virtual machine (VM) resources in Cloud computing.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.2
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• pp.353-360
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• 2017

The Internet of Things(IoT) security has more need than a technical problem as it needs series of regulations and faultless security system for common purposes. So, this study proposes an efficient key management in order that can be trusted IoT data in cloud computing. In contrast with a key distribution center of existing sensor networks, the proposed a federation key management of cloud proxy key server is not central point of administration and enables an active key recovery and update. The proposed key management is not a method of predetermined secret keys but sharing key information of a cloud proxy key server in autonomous cloud, which can reduce key generation and space complexity. In addition, In contrast with previous IoT key researches, a federation key of cloud proxy key server provides an extraction ability from meaningful information while moving data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.9
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• pp.2114-2120
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• 2015

Vehicle cloud technology is a fusion technology of vehicle communication technology and cloud computing used in wired and wireless Internet, and has attracted attention as a new IT paradigm. It is expected that it would contribute to resolve the road traffic problem with effective communication by providing computer, sensor, communication, device, and resource. but security is necessary to apply vehicle cloud environment and it have to resolve security threats and various attacks occurred in wired and wireless vehicle environment. Therefore, in this paper, we designed security framework to provide secure communication between vehicle and vehicle, and vehicle and the Road side in the vehicle cloud environment. Safety and security of the vehicle environment was satisfied with the security requirements of the vehicle and cloud-based environment, and increased efficiency than the conventional vehicle network communication protocols.

• Korean Journal of Remote Sensing
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• v.20 no.4
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• pp.235-244
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• 2004

This study focuses on the assessment for proposed algorithm to discriminate cloudy pixels from snowy pixels through use of visible, near infrared, and short wave infrared channel data in VEGETATION-1 sensor embarked on SPOT-4 satellite. Traditional threshold algorithms for cloud and snow masks did not show very good accuracy. Instead of these independent masking procedures, K-Means clustering scheme is employed for cloud/snow discrimination in this study. The pixels used in clustering were selected through an integration of two threshold algorithms, which group ensemble the snow and cloud pixels. This may give a opportunity to simplify the clustering procedure and to improve the accuracy as compared with full image clustering. This paper also compared the results with threshold methods of snow cover and clouds, and assesses discrimination capability in VEGETATION channels. The quality of the cloud and snow mask even more improved when present algorithm is implemented. The discrimination errors were considerably reduced by 19.4% and 9.7% for cloud mask and snow mask as compared with traditional methods, respectively.

• International journal of advanced smart convergence
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• v.10 no.4
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• pp.278-288
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• 2021

Recently, IoT systems are cloud-based, so that continuous and large amounts of data collected from sensor nodes are processed in the data server through the cloud. However, in the centralized configuration of large-scale cloud computing, computational processing must be performed at a physical location where data collection and processing take place, and the need for edge computers to reduce the network load of the cloud system is gradually expanding. In this paper, a cluster system consisting of 6 inexpensive Raspberry Pi boards was constructed to perform fast data processing. And we propose "Kubernetes cluster system(KCS)" for processing large data collection and analysis by model distribution and data pipeline method. To compare the performance of this study, an ensemble model of deep learning was built, and the accuracy, processing performance, and processing time through the proposed KCS system and model distribution were compared and analyzed. As a result, the ensemble model was excellent in accuracy, but the KCS implemented as a data pipeline proved to be superior in processing speed..

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.6
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• pp.1287-1292
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• 2018

In case of fire, the information related with the fire need to be provided to fire fighters in order to extinguish the fire efficiently. However, existing fire detection systems have the problem not to provide the data of the fire to fire fighters visually. In this paper, we propose the fire spot system using a cloud server to solve this problem. In the proposed system, the sensors installed in a building collect the gas and temperature data and store them into the cloud database using a wireless network. For fire fighters in the field, the details and history of the fire spot are displayed visually on top of the blue print retrieved from the cloud database using a smart device.

• The Journal of Korea Robotics Society
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• v.17 no.3
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• pp.373-380
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• 2022

This paper considers a method of fast correspondence matching for iterative closest point (ICP) algorithm. In robotics, the ICP algorithm and its variants have been widely used for pose estimation by finding the translation and rotation that best align two point clouds. In computational perspectives, the main difficulty is to find the correspondence point on the reference point cloud to each observed point. Jump-table-based correspondence matching is one of the methods for reducing computation time. This paper proposes a method that corrects errors in an existing jump-table-based correspondence matching algorithm. The criterion activating the use of jump-table is modified so that the correspondence matching can be applied to the situations, such as point-cloud registration problems with highly curved surfaces, for which the existing correspondence-matching method is non-applicable. For demonstration, both hardware and simulation experiments are performed. In a hardware experiment using Hokuyo-10LX LiDAR sensor, our new algorithm shows 100% correspondence matching accuracy and 88% decrease in computation time. Using the F1TENTH simulator, the proposed algorithm is tested for an autonomous driving scenario with 2D range-bearing point cloud data and also shows 100% correspondence matching accuracy.